Architectural heritage images classification using deep learning with CNN
Abed, Mohammed Hamzah, Al-Asfoor, Muntasir and Hussain, Zahir M. (2020) Architectural heritage images classification using deep learning with CNN. In: CEUR Workshop Proceedings.
Full text not available from this repository. (Request a copy)Abstract
Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and enhance the search of a specific item, which helps in the tasks of studying and analysis the proper heritage object. Deep learning for architectural heritage images classification has been employed during the course of this study. The pre-trained convolutional neural networks GoogLeNet, resnet18 and resnet50 proposed to be applied on public dataset Cultural Heritage images. Experimental results have shown promising outcomes with an accuracy of “87.91”, “95.47” and “95.57” respectively. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Cited by: 11; Conference name: 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, VIPERC 2020; Conference date: 29 January 2020; Conference code: 159745 |
| Keywords: | Classification (of information), Convolution, Convolutional neural networks, Digital libraries, Extraction, Image analysis, Image classification, Image enhancement, Architectural heritage, Cultural heritages, Digital documentation, Heritage objects, Images classification, Public dataset, Research papers, Deep learning |
| Depositing User: | RED Unit Admin |
| Date Deposited: | 13 Mar 2025 12:23 |
| Last Modified: | 13 Mar 2025 12:23 |
| URI: | https://bnu.repository.guildhe.ac.uk/id/eprint/19671 |
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